Import Necessary libraries

Model Architecture

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Model creation

Loading Lane Dataset

preprocessing

create train test split

ploting some train data

Data Augmentation

Overfitting often occurs when we have a small number of training examples. One way to fix this problem is to augment our dataset so that it has sufficient number and variety of training examples. Data augmentation takes the approach of generating more training data from existing training samples, by augmenting the samples through channel_shift_range that yield believable-looking images. The goal is that at training time, your model will never see the exact same picture twice. This exposes the model to more aspects of the data, allowing it to generalize better.

Model Training

Saving the trained model

Visualizing results of the training

visualize the results we get after training our network.

Trying some Tests

Viewing test video after applying the trained model